An improved image wrapping method that preserves visually prominent features while resizing image into arbitrary scaling ratios is proposed. In this method, a salient context aware model is adopted to construct salient map to sign prominent image features at the aspect of texture and semantics. The salient map contains not only prominent texture with strong gradient changes but salient region with significant semantics. This model is implemented with an end-to-end convolutional neural network which maps input image to corresponding feature salient features. The proposed method simultaneously learns detailed and salient context and fuse both features as the salience mask. Then the seam carving method is utilized to implement image resizing with arbitrary ratios combined with the salience mask. Experimental results of the proposed and classic seam carving method indicate that our formulation has more robust and effective performance.
Gray code assisted phase shifting technology can achieve robust and noise-tolerated three-dimensional (3D) shape measurements. To solve the issues of unsynchronized brightness changes, local overexposure, and edge coding errors caused by inconsistent reflectivity of the surface in complex industrial scenes, as well as defocusing caused by noncontinuous surfaces and varying distances, we combine the advantages of a large imaging range in passive stereo vision and high precision in active structured light imaging. It uses a consumer-grade projector to project gray code and stripe patterns, whereas two precalibrated color industrial cameras capture raw images and obtain the original channel data. Gray code and reverse gray code images are projected to solve the problems of binarization and boundary blur. In addition, an error point filtering strategy is proposed to retain pixels with decoding errors of less than two bits. The use of softargmin for subpixel matching of absolute phase results in a high precision disparity map. We present a simple and high precision 3D measurement system for industrial objects. Experiments on 3D measurements in complex industrial scenes showed that the proposed method can achieve high precision and robust 3D shape measurements.
The use of spatial coding schemes is always a research hotspot of structural light 3D reconstruction. Spatial coding only needs one frame of image to reshape the three-dimensional feature of the object. However, it is difficult to obtain higher resolution due to fewer feature points extracted. In the coding stage, this paper uses a two-dimensional discrete pseudorandom pattern composed of rectangular color elements. And in the decoding stage, a feature detector for a rectangular grid point and a center point is proposed by using four corner points and a center point of a rectangle as feature points. It can get more feature points in the spatial coding without increasing the calculation amount during the decoding stage, thereby obtaining more accurate feature information on the surface of the object. From the experimental results, this method compared with the existing approaches can significantly improve the accuracy of rectangular grid points detection and can reconstruct more high-precision feature points.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.